Potential accuracy of image orientation of small satellites: a case study of CHRIS/Proba data
Bibliographic record
Abstract
Abstract The new technology of small satellites (microsatellites) opens a new era in satellite observation of the earth. Small satellites such as ESA’s Project for On‐Board Autonomy (Proba), launched on 22nd October 2001, are of interest due to their low cost, flexibility of positioning, and capability for multiangular scanning in both across‐track and along‐track directions. Proba’s Compact High Resolution Imaging Spectrometer (CHRIS) provides multidirectional, as well as hyperspectral, data at 18 m resolution and is supplied mainly to the scientific community for experimental environmental applications. This research evaluates the use of various empirical mathematical models for small satellite orientation and terrain modelling using the multidirectional viewing capabilities of CHRIS data. Geometric correction and co‐registration of multiangle images is essential for their use for data extraction. Ideally, rigorous mathematical models should be formulated which precisely describe the satellite motion and represent the relationship between the image and the object spaces. The use of rigorous mathematical models has not been fully investigated using CHRIS/Proba data, because the satellite information provided is not adequate for rigorous sensor modelling. In this paper, several alternative empirical models are tested for the orientation and 3D geopositioning of CHRIS sensor images. The images used in this study cover extremely mountainous terrain in central Hong Kong. A set of five images from CHRIS/Proba taken in December 2005 from different angles are used to test the applicability of different forms of the empirical models for 3D geopositioning. The accuracy of the models is tested for different numbers and distribution of ground control points (GCPs) using different combinations of observation angles and base‐to‐height ratios. The results obtained show high integrity of the models used for CHRIS/Proba image orientation. In some cases, accuracy better than 2 pixels can be achieved using a modest number of GCPs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".